Problem-Oriented Medical Information System (PROMIS)

The Problem-Oriented Medical Information System, or PROMIS, was an early EHR system developed at the University of Vermont in 1976 by Dr. Lawrence L. Weed and Jan Schultz.

PROMIS was an interactive, touch screen system that allowed users to access a medical record within a large body of medical knowledge. At its peak, the PROMIS system had over 60,000 frames of knowledge. PROMIS was also known for its fast responsiveness, especially for its time.

Contents

History

From 1969 to 1982, Dr. Lawrence L. Weed worked at the University of Vermont to computerize the problem-oriented medical record (POMR). He recognized that the mind of the physician could not effectively process the large amount of information received, and this could interfere with the care of the patient. He began to organize the data temporarily to make them more available to the physician. This pairing of data led to the development of a commercial product, Problem - Knowledge Couplers (PKC - [1]).

PROMIS was one of the first electronic medical records (EMR) to implement support from other parts of the medical community (e.g., pharmacy and nursing). A patient’s profile could be accessed at any terminal with little delay in the transfer of the information, making healthcare more efficient. [Schultz 1988]

The system consisted of several dozen computer terminals which fed into the central memory unit. A terminal was wheeled to a patient's bedside where the patient with the help of a nurse would enter his or her medical history via touch-sensitive screen. The physican would then review the entries and formulated the problems.

The introduction of the problem-oriented medical record (POMR) by Lawrence Weed (1969) influenced medical thinking about both manual and automated medical records. By suggesting that the primary organization of the medical record should be by the medical problem and all diagnostic and therapeutic plans should be linked to a specific problem, Weed was one of the people who recognize the importance of an internal structure of a medical record either stored on paper or in a computer.

PROMIS was developed at the University of Vermont in 1976, by Jan Schultz and Dr. Larry Weed. This was achieved by first developing The Medical Knowledge Database which consisted of traditional knowledge from journals along with other written materials housed in frames and patient specific knowledge from electronic patient records. Once the data input was done, more frames for drug information, diagnostic process, radiology reporting were created. With all this overflow of information a container called “branching information table” was created which was stored information sorted by problem specific information.[Schultz 1988]

PROMIS was successfully implemented in Medical and Gynecological wards in Medical Center Hospital in Vermont. More than 3000 patients record was added in the span of 4 years from 1970 to 1974 and more than 500 medical personnels used PROMIS on daily basis. With committed hardware support system PROMIS was easily accessible by the staff. [Schultz 1988]

In 1973, it was decided to use PROMIS for entire Hospital. The existing system was not capable to support that expanse of data, which lead to development of additional architecture. A network of minicomputers with nodes was designed, new hardware and software was procured. FORTRAN programming language was installed on V77-400 minicomputers in the network.The newly developed PROMIS was more efficient.[Schultz 1988]

The PROMIS system has been installed at the Baycrest Geriatric Hospital in Toronto, Canada.[Schultz 1988]

Apparently, the developers of Carnegie Mellon University's ZOG system were so impressed with PROMIS that it reinspired them to return to their own work.

Early Contributors

Dr. Lawrence Weed recognizes the vast contributions of Dr. Harold Cross, MD in setting up a problem-oriented medical practice. Along with the contributions of Dr. John Bjorn, MD and Dr. Charles Burger, MD, a problem-oriented medical practice was established as a proven model that would form the basis of electronic record systems with patient problem orientation.

Dr. Charles Burger, who himself was influential in the development of the decision tool called problem knowledge coupler, recognizes the pioneers of the discipline: "It is impossible to overstate the contribution these men made to the acceptance and spread of the problem-oriented system." - Charles Burger, MD.

Issues

Computer-Based POMR methodology

PKC’s insightful 1998 white paper on Problem Oriented Medical Records (POMR), entitled, “A Problem Oriented Approach to the Computerized Patient Record,” illuminates some of the strengths and weaknesses of the methodology.[PKC paper] Classically, POMR information is organized under four sections:

This structured approach is well suited to a computer-based implementation. On the other hand, the challenge is to view the problem lists holistically rather than discretely. Plans for one problem may preclude action on another: priorities must be set and interactions considered. PKC partly addresses this by recommending a list of “assets” (e.g., positive mental, emotional, and physical attributes) be used to help make a balanced judgment of the patient’s potential response to the presumed illness or its suggested treatment. But the POMR model remains oriented around discrete information that is not well integrated in a holistic fashion.

Difficulty with Ambiguity

An example of this tension occurs in the computer-based POMR where data must be stored in discrete and unambiguous terms. This results in a loss of information: the rich “analog” data of life are represented in the computer with discrete “digital” data; and complex inter-related body functions are organized into discrete “silos” of information. PKC again partly addresses this issue by recognizing the necessity of free text, but even this cannot replace the richness of information stored in an image, such as in a CT-scan or an MRI or a high-quality color photograph of a wound.

Abstraction of Medical data

In addition, the structuring of data in the computer in a hierarchical fashion, as described by PKC, further abstracts and artificially isolates medical data. The elements in one hierarchical list may interact with those on another list, but the list structure itself imposed by the computer model may obscure such interdependent relationships. As a result the computer-based POMR is optimized for Boolean searches and hierarchical analysis, which work well for computers, but such constructs may hinder solutions to complex problems in living systems.
MikeField 09:21, 17 January 2010 (CST)